水力发电学报2017，Vol.36Issue(8)：12-21,10.DOI:10.11660/slfdxb.20170802
基于联合互信息的水文预报因子集选取研究
Selecting hydrological forecast factor sets based on joint mutual information
摘要
Abstract
A forecast factor set,or a certain combination of forecast factors,is crucial to forecasting accuracy,since its size determines the number of information sources for the forecasting.Considering the shortcoming in previous methods that focused on the cases of one single factor,this paper,from a holistic perspective,presents a method for selecting hydrological forecast factor set based on joint mutual information.First,we introduce the concept of mutual information and extend it to high-dimensional cases.Both conditional mutual information and joint mutual information are illustrated and calculated by the Parzen window estimation method.Then,using conditional mutual information,a maximum joint mutual information (JMI) model is constructed and solved for application of hydrological forecasting.Finally,the results of this method are tested via calculations of back propagation (BP) neural network and compared with those of previous methods in a case study of the Luning hydrological station in the Yalong River basin.This work shows that the new method can generate more appropriate inputs for hydrological forecasting models.关键词
水文预报/预报因子集/条件互信息/联合互信息Key words
hydrological forecast/forecast factor set/conditional mutual information/joint mutual information分类
天文与地球科学引用本文复制引用
纪昌明,俞洪杰,阎晓冉,李荣波,王丽萍..基于联合互信息的水文预报因子集选取研究[J].水力发电学报,2017,36(8):12-21,10.基金项目
“十三五”国家重点研发计划课题(2016YFC0402208) （2016YFC0402208）
中央高校基本科研业务费专项(2016XS46 （）
2016MS51) （）
